DP defines an upper bound of the privacy level by computing a pair of \((\epsilon , \delta (\epsilon ))\)  on the privacy budget.
Lower \(\epsilon \)  values indicate better privacy protection but also potentially the less useful the protected algorithm is.
The value \(\delta \)  can be interpreted as the probability of failing to achieve DP.
With the notion of the neighboring data sets, i.e., \(X\sim X^{\prime }\) , which differs by one data record,
the formal \((\epsilon , \delta )\) -DP definition is given below [1]}.
